Given a new security vulnerability is identified
When the issue is logged in Jira
Then the issue should:
  - Have a unique identifier
  - Be assigned to the appropriate security team member
  - Include severity and impact classification
  - Allow detailed description and reproduction steps

Given a security vulnerability issue
When the issue moves through different workflow states
Then the system should:
  - Enforce appropriate permissions for state transitions
  - Log all state changes
  - Notify relevant stakeholders
  - Prevent unauthorized modifications

Given a new software architecture design
When ThreatModeler analyzes the system
Then it should:
  - Automatically generate a comprehensive threat model
  - Identify potential attack vectors
  - Provide risk severity ratings
  - Suggest mitigation strategies

Given a completed threat model
When the report is generated
Then the output should:
  - Be compatible with STRIDE methodology
  - Include detailed risk descriptions
  - Provide actionable remediation recommendations
  - Allow export to standard formats (PDF, XML)

Given a complex system architecture
When a security workflow diagram is created
Then the diagram should:
  - Clearly represent all system components
  - Show data flow and potential security boundaries
  - Include color-coded risk indicators
  - Support collaborative editing

Given a security workflow diagram
When risk analysis is performed
Then the diagram should:
  - Highlight potential vulnerabilities
  - Allow annotation of security controls
  - Support real-time collaboration
  - Enable version tracking

Given a new security document
When the document is created in Confluence
Then the system should:
  - Enforce strict access controls
  - Log all document access and modifications
  - Support versioning and rollback
  - Encrypt sensitive information

Given multiple security documents
When an audit is conducted
Then the system should:
  - Provide comprehensive access logs
  - Support compliance reporting
  - Enable granular permission management
  - Facilitate secure document sharing

Given a new security issue
When the issue is created in GitHub
Then the system should:
  - Link directly to specific code commits
  - Support labeling and categorization
  - Enable cross-repository references
  - Provide notification mechanisms

Given a security task in GitHub Issues
When team members interact with the issue
Then the system should:
  - Support comments and discussions
  - Track issue status and progression
  - Allow assignment and reassignment
  - Integrate with CI/CD pipelines

Feature: GitGuardian Non-Human Identity Security 
Scenario: Detect and manage NHI secrets and relationships Given a repository or system with machine identities and secrets, When GitGuardian performs scanning 

- Detect and locate secrets tied to NHIs (e.g., API keys, tokens). 
- Map the connections and relationships between NHIs. 
- Provide real-time alerts for exposed secrets or anomalies. 
- Identify secrets stored outside secure vaults. 
- Offer visibility into the origins and permissions of each secret.

Feature: GitGuardian NHI Governance Solution 
Scenario: Manage and mitigate risks associated with NHIs and their secrets 
Given the detection of NHI secrets and their dependencies, When GitGuardian triggers remediation workflows, Then the system should:

-  Map all active relationships between NHIs. 
-  Automatically recommend or enforce rotation of aged or exposed secrets. 
-  Suggest best practices to store and manage secrets securely. 
-  Notify teams with incident insights and remediation guidance. 
-  Flag over-privileged or unused NHIs for review or decommissioning

Feature: Snyk Dependency Security Scanning
Scenario: Identify and assess vulnerabilities in project dependencies
  Given a project with multiple open-source dependencies
  When Snyk scans the project
  Then the system should:
    - Detect known security vulnerabilities
    - Provide CVSS severity ratings
    - Offer precise remediation recommendations
    - Support multiple programming languages
    - Generate comprehensive vulnerability reports

Feature: Snyk Vulnerability Remediation
Scenario: Automatic vulnerability fix suggestions
  Given detected vulnerabilities in dependencies
  When Snyk analyzes the issues
  Then the system should:
    - Suggest specific version upgrades
    - Provide patch recommendations
    - Enable automatic dependency updates
    - Create pull requests with fixes
    - Prioritize critical security issues

Feature: Checkmarx Code Security Analysis
Scenario: Perform thorough static code analysis
  Given a complete codebase
  When Checkmarx performs security scanning
  Then the system should:
    - Identify potential security vulnerabilities
    - Categorize risks by severity
    - Provide precise code-level recommendations
    - Support multiple programming languages
    - Generate detailed vulnerability reports

Feature: Checkmarx Development Workflow Integration
Scenario: Seamless security scanning in CI/CD pipeline
  Given a code commit in the repository
  When Checkmarx is triggered
  Then the system should:
    - Automatically scan new and modified code
    - Block builds with critical vulnerabilities
    - Generate real-time security feedback
    - Integrate with version control systems
    - Provide developer-friendly remediation guidance

Feature: SonarQube Code Quality Scanning
Scenario: Evaluate code quality and security
  Given a project codebase
  When SonarQube performs analysis
  Then the system should:
    - Identify code quality issues
    - Detect potential security vulnerabilities
    - Calculate technical debt
    - Provide maintainability ratings
    - Support multiple programming languages

Feature: SonarQube Quality Gates
Scenario: Enforce code quality standards
  Given a code commit
  When SonarQube quality gates are applied
  Then the system should:
    - Block commits not meeting quality thresholds
    - Provide detailed quality metrics
    - Support custom quality rules
    - Generate comprehensive compliance reports
    - Offer trend analysis of code quality

Feature: Semgrep Custom Security Rules
Scenario: Perform targeted code vulnerability scanning
  Given a custom security ruleset
  When Semgrep analyzes the codebase
  Then the system should:
    - Support custom, language-specific rules
    - Perform fast, lightweight scanning
    - Identify security and code quality issues
    - Generate detailed findings
    - Support multiple programming languages

Feature: Semgrep Rule Management
Scenario: Create and apply custom security rules
  Given a security requirement
  When a custom Semgrep rule is created
  Then the system should:
    - Allow creation of complex rule patterns
    - Support multiple rule configurations
    - Enable easy rule sharing
    - Provide rule testing mechanisms
    - Integrate with CI/CD pipelines

Feature: Jenkins Security Pipeline Configuration
Scenario: Validate secure build process
  Given a new software build configuration
  When Jenkins executes the build pipeline
  Then the system should:
    - Enforce role-based access controls
    - Implement credential management
    - Scan for potential security vulnerabilities
    - Generate comprehensive build logs
    - Support secure parameter handling

Feature: Jenkins Security Plugin Validation
Scenario: Verify security plugin functionality
  Given multiple security plugins are installed
  When a build is triggered
  Then the system should:
    - Perform static code analysis
    - Check dependency vulnerabilities
    - Validate configuration compliance
    - Generate security reports
    - Block builds with critical vulnerabilities

Feature: GitLab Security Build Integration
Scenario: Execute security-enhanced build process
  Given a code repository with CI/CD configuration
  When GitLab executes the build pipeline
  Then the system should:
    - Perform automated security scanning
    - Validate code quality metrics
    - Generate comprehensive security reports
    - Support parallel security testing
    - Provide real-time vulnerability feedback

Feature: GitLab Compliance Validation
Scenario: Ensure build process meets security standards
  Given organizational security requirements
  When GitLab CI/CD pipeline is executed
  Then the system should:
    - Enforce predefined security policies
    - Block non-compliant builds
    - Generate audit trails
    - Support custom compliance rules
    - Provide detailed violation reports

Feature: CircleCI Security Configuration
Scenario: Validate secure build environment
  Given a complex build configuration
  When CircleCI executes the build
  Then the system should:
    - Implement isolated build environments
    - Manage secret and credential injection
    - Perform automated security checks
    - Support granular access controls
    - Generate comprehensive build reports

Feature: CircleCI Artifact Security
Scenario: Manage and secure build artifacts
  Given build artifacts generated
  When artifacts are processed
  Then the system should:
    - Implement artifact scanning
    - Enforce access controls
    - Detect potential security risks
    - Support artifact encryption
    - Provide detailed artifact provenance

Feature: Trivy Container Image Vulnerability Detection
Scenario: Scan Docker image for vulnerabilities
  Given a Docker container image
  When Trivy performs security scanning
  Then the system should:
    - Identify known vulnerabilities
    - Provide CVSS severity ratings
    - Support multiple image formats
    - Generate detailed vulnerability reports
    - Offer remediation recommendations

Feature: Trivy Continuous Security Monitoring
Scenario: Integrate vulnerability scanning in build process
  Given a build pipeline
  When Trivy is integrated
  Then the system should:
    - Perform real-time image scanning
    - Block builds with critical vulnerabilities
    - Support custom severity thresholds
    - Generate comprehensive security reports
    - Provide actionable remediation guidance

Feature: Anchore Container Policy Enforcement
Scenario: Apply security policies to container images
  Given custom security policies
  When Anchore evaluates container images
  Then the system should:
    - Enforce predefined security rules
    - Detect policy violations
    - Support complex policy configurations
    - Generate detailed compliance reports
    - Block non-compliant container deployments

Feature: Anchore Comprehensive Vulnerability Scanning
Scenario: Perform in-depth container image analysis
  Given a container image
  When Anchore performs scanning
  Then the system should:
    - Identify known and unknown vulnerabilities
    - Analyze package dependencies
    - Provide risk scoring
    - Support multiple image formats
    - Generate actionable remediation recommendations

Feature: OWASP ZAP Vulnerability Detection
Scenario: Perform full web application security assessment
  Given a target web application
  When OWASP ZAP conducts a comprehensive scan
  Then the system should:
    - Detect OWASP Top 10 vulnerabilities
    - Perform automated penetration testing
    - Generate detailed vulnerability reports
    - Identify potential security risks
    - Provide actionable remediation guidance

Feature: OWASP ZAP Advanced Security Testing
Scenario: Execute multi-layered security assessment
  Given a complex web application
  When ZAP performs advanced scanning
  Then the system should:
    - Support multiple scanning strategies
    - Conduct authenticated and unauthenticated scans
    - Detect hidden vulnerabilities
    - Simulate various attack scenarios
    - Generate comprehensive security insights

Feature: Burp Suite Comprehensive Vulnerability Scanning
Scenario: Perform in-depth web application security testing
  Given a target web application
  When Burp Suite conducts security assessment
  Then the system should:
    - Identify complex security vulnerabilities
    - Perform detailed application mapping
    - Support manual and automated testing
    - Generate comprehensive vulnerability reports
    - Provide advanced exploitation analysis

Feature: Burp Suite Penetration Testing Capabilities
Scenario: Execute advanced security testing
  Given a web application with complex architecture
  When Burp Suite performs penetration testing
  Then the system should:
    - Simulate sophisticated attack vectors
    - Detect subtle security weaknesses
    - Support custom testing scenarios
    - Provide detailed exploit information
    - Generate actionable security recommendations

Feature: SAST Code Vulnerability Detection
Scenario: Perform static code security analysis
  Given a complete codebase
  When SAST tool scans the code
  Then the system should:
    - Identify potential security vulnerabilities
    - Analyze code across multiple languages
    - Provide precise vulnerability locations
    - Generate detailed remediation recommendations
    - Support custom security rule configurations

Feature: SAST Security Policy Validation
Scenario: Enforce security standards in code
  Given organizational security policies
  When SAST tool analyzes the codebase
  Then the system should:
    - Validate code against security standards
    - Block commits with critical vulnerabilities
    - Generate comprehensive compliance reports
    - Support custom security rules
    - Provide actionable developer guidance

Feature: DAST Comprehensive Security Scanning
Scenario: Perform dynamic security assessment
  Given a live web application
  When DAST tool conducts scanning
  Then the system should:
    - Detect runtime security vulnerabilities
    - Simulate various attack scenarios
    - Provide real-time vulnerability insights
    - Support complex web application architectures
    - Generate detailed security reports

Feature: DAST Advanced Security Verification
Scenario: Execute advanced security testing
  Given a target web application
  When DAST tool performs comprehensive testing
  Then the system should:
    - Identify complex security weaknesses
    - Support authenticated and unauthenticated scans
    - Provide detailed vulnerability analysis
    - Simulate advanced attack vectors
    - Generate actionable remediation guidance

Feature: Kali Linux Security Assessment
Scenario: Perform in-depth security penetration testing
  Given a target system or application
  When Kali Linux tools conduct security assessment
  Then the system should:
    - Support multiple penetration testing techniques
    - Identify hidden security vulnerabilities
    - Provide detailed exploitation capabilities
    - Generate comprehensive security reports
    - Support various testing scenarios

Feature: Kali Linux Advanced Security Testing
Scenario: Execute comprehensive security assessment
  Given a complex network or application environment
  When Kali Linux tools perform security testing
  Then the system should:
    - Conduct network and application mapping
    - Identify potential entry points
    - Support advanced exploitation techniques
    - Generate detailed security intelligence
    - Provide actionable security recommendations

Feature: HashiCorp Vault Secrets Management
Scenario: Secure Secret Lifecycle Management
  Given multiple deployment environments
  When secrets are managed through Vault
  Then the system should:
    - Encrypt and securely store sensitive credentials
    - Support dynamic secret generation
    - Implement fine-grained access controls
    - Provide comprehensive audit logging
    - Enable automatic secret rotation
    - Support multi-cloud and hybrid environments

Feature: AWS Inspector Deployment Security Validation
Scenario: Comprehensive Deployment Security Assessment
  Given a new AWS deployment
  When AWS Inspector performs security scan
  Then the system should:
    - Identify potential security vulnerabilities
    - Assess network accessibility
    - Check against industry security benchmarks
    - Generate detailed remediation recommendations
    - Support continuous security monitoring

Feature: Aqua Security Container Deployment Validation
Scenario: Secure Container Deployment Protection
  Given containerized application deployment
  When Aqua Security performs assessment
  Then the system should:
    - Scan container images for vulnerabilities
    - Enforce runtime security policies
    - Detect and prevent unauthorized container activities
    - Provide comprehensive compliance reporting
    - Support multi-cloud container environments

Feature: Kubernetes Security Compliance Assessment
Scenario: Comprehensive Kubernetes Security Validation
  Given a Kubernetes cluster deployment
  When Kube-bench performs security assessment
  Then the system should:
    - Validate cluster against CIS security benchmarks
    - Identify security misconfigurations
    - Provide detailed remediation recommendations
    - Support multiple Kubernetes deployment types
    - Generate comprehensive compliance reports
    - Assess both master and worker node configurations

Feature: Ansible Vault Sensitive Configuration Management
Scenario: Secure Deployment Configuration Handling
  Given sensitive deployment configurations
  When Ansible Vault manages the configurations
  Then the system should:
    - Encrypt sensitive configuration files
    - Support granular access controls
    - Enable secure credential management
    - Provide audit trails for configuration access
    - Support seamless integration with deployment workflows
    - Allow secure sharing of encrypted configurations

Feature: Datadog Security and Performance Monitoring
Scenario: Advanced Infrastructure Monitoring
  Given a complex multi-cloud infrastructure
  When Datadog performs monitoring
  Then the system should:
    - Detect unusual system behavior
    - Generate real-time security alerts
    - Provide comprehensive performance metrics
    - Support cross-platform monitoring
    - Enable proactive threat detection

Feature: Splunk Threat Detection and Log Analysis
Scenario: Comprehensive Security Event Monitoring
  Given multiple system logs and event sources
  When Splunk performs analysis
  Then the system should:
    - Correlate security events across systems
    - Detect potential security incidents
    - Generate comprehensive threat reports
    - Support real-time alerting
    - Provide advanced forensic capabilities

Feature: ELK Stack Log Analysis and Threat Detection
Scenario: Advanced Log Management and Security Insights
  Given multiple system and application logs
  When ELK Stack processes the logs
  Then the system should:
    - Collect logs from diverse sources
    - Perform real-time log parsing and indexing
    - Create interactive visualizations
    - Detect potential security anomalies
    - Support complex query and filtering mechanisms
    - Generate comprehensive threat intelligence reports

Feature: Sysdig Container Environment Monitoring
Scenario: Comprehensive Container Security and Performance Analysis
  Given a containerized application environment
  When Sysdig performs monitoring
  Then the system should:
    - Provide real-time container visibility
    - Detect abnormal container behaviors
    - Monitor container performance metrics
    - Identify potential security vulnerabilities
    - Support multi-cloud and hybrid environments
    - Generate detailed container-level insights

Feature: PagerDuty Incident Management and Alerting
Scenario: Real-time Critical Issue Notification
  Given multiple monitoring sources
  When critical issues are detected
  Then the system should:
    - Send immediate, prioritized alerts
    - Support multi-channel notification
    - Enable escalation policies
    - Provide incident tracking and management
    - Support on-call scheduling
    - Generate comprehensive incident reports

Feature: Prometheus System Monitoring and Alerting
Scenario: Advanced Performance and Security Monitoring
  Given a distributed system infrastructure
  When Prometheus performs monitoring
  Then the system should:
    - Collect comprehensive performance metrics
    - Generate intelligent alerts
    - Support multi-dimensional data collection
    - Provide real-time system health insights
    - Enable custom monitoring configurations

Feature: New Relic Application Health Monitoring
Scenario: Comprehensive Application Performance Assessment
  Given a complex distributed application
  When New Relic performs monitoring
  Then the system should:
    - Track application performance metrics
    - Detect potential performance bottlenecks
    - Identify security-related performance issues
    - Generate detailed performance reports
    - Support real-time alerting mechanisms

Feature: Nagios Comprehensive System Monitoring
Scenario: Advanced Infrastructure Performance Tracking
  Given a complex IT infrastructure
  When Nagios performs monitoring
  Then the system should:
    - Monitor multiple systems and network devices
    - Generate real-time performance alerts
    - Support custom monitoring plugins
    - Provide detailed performance reporting
    - Enable proactive issue detection
    - Support distributed monitoring architectures

Feature: Cloudflare DDoS Protection and Security
Scenario: Comprehensive Web Application Security
  Given a web application infrastructure
  When Cloudflare provides protection
  Then the system should:
    - Detect and mitigate DDoS attacks
    - Provide real-time threat intelligence
    - Implement web application firewall
    - Support SSL/TLS encryption
    - Generate detailed security reports
    - Optimize application performance

Feature: Falco Container Anomaly Detection
Scenario: Advanced Container Security Monitoring
  Given a containerized application environment
  When Falco performs monitoring
  Then the system should:
    - Detect suspicious container activities
    - Provide real-time threat alerts
    - Support custom security rules
    - Monitor system calls and container behaviors
    - Generate comprehensive security reports
    - Integrate with container orchestration platforms

Feature: ModSecurity Virtual Patching Capabilities
Scenario: Dynamic Vulnerability Protection
  Given a web application with known vulnerabilities
  When ModSecurity implements virtual patch
  Then the system should:
    - Detect and block potential exploit attempts
    - Apply rules without application code modifications
    - Support custom rule creation
    - Provide real-time threat detection
    - Generate comprehensive security logs
    - Minimize false positive rates

Feature: FortiWeb Dynamic Security Patching
Scenario: Comprehensive Vulnerability Mitigation
  Given multiple web application security risks
  When FortiWeb applies virtual patches
  Then the system should:
    - Automatically detect emerging vulnerabilities
    - Apply context-aware security rules
    - Support machine learning-based threat detection
    - Provide zero-day vulnerability protection
    - Generate detailed security analytics
    - Enable seamless application continuity

Feature: Imperva Virtual Patching Effectiveness
Scenario: Advanced Threat Mitigation
  Given complex web application environment
  When Imperva WAF implements security rules
  Then the system should:
    - Detect and block sophisticated attack vectors
    - Apply granular security policies
    - Support application-specific virtual patching
    - Provide real-time threat intelligence
    - Minimize performance overhead
    - Enable rapid vulnerability response

Feature: F5 Advanced WAF Virtual Patching
Scenario: Automated Vulnerability Management
  Given diverse application portfolio
  When F5 WAF applies security patches
  Then the system should:
    - Automatically identify potential vulnerabilities
    - Apply context-aware security rules
    - Support rapid threat response
    - Generate comprehensive security reports
    - Provide minimal false positive detection
    - Enable seamless security updates

Feature: Cloud-based Virtual Patching Deployment
Scenario: Global Threat Mitigation
  Given distributed web application infrastructure
  When Akamai implements security rules
  Then the system should:
    - Deploy security patches globally
    - Provide near-instantaneous threat response
    - Support multi-cloud and hybrid environments
    - Generate comprehensive threat intelligence
    - Minimize latency and performance impact
    - Enable adaptive security configurations

import sagemaker
from sagemaker.inputs import TrainingInput
from sagemaker.xgboost import XGBoost

# SageMaker session and role
sagemaker_session = sagemaker.Session()
role = sagemaker.get_execution_role()

# Dataset S3 location
input_data = sagemaker_session.upload_data(
    path='titanic.csv', 
    bucket=sagemaker_session.default_bucket(), 
    key_prefix='titanic/input'
)

# Training job with encryption enabled
xgboost = XGBoost(
    entry_point='train.py',
    framework_version='1.3-1',
    py_version='py3',
    role=role,
    instance_count=1,
    instance_type='ml.m5.large',
    sagemaker_session=sagemaker_session,
    output_path=f's3://{sagemaker_session.default_bucket()}/titanic/output',
    encrypt_inter_container_traffic=True
)

# Start training
xgboost.fit({'train': TrainingInput(input_data, content_type="text/csv")})
print("Training complete with encryption.")

import kfp
from kfp.dsl import pipeline

@pipeline(name='multi-tenant-pipeline')
def tenant_pipeline(dataset_path: str):
    # Load dataset
    load_data = dsl.ContainerOp(
        name='Load Data',
        image='tensorflow/tensorflow:latest',
        command=['python', 'load_data.py'],
        arguments=['--path', dataset_path]
    )
    # Train model
    train_model = dsl.ContainerOp(
        name='Train Model',
        image='tensorflow/tensorflow:latest',
        command=['python', 'train.py'],
        arguments=['--dataset', load_data.output]
    )
    train_model.add_volume(volume)
    train_model.add_node_selector_constraint('kubernetes.io/hostname', 'tenant-node')

pipeline_func = tenant_pipeline
kfp.Client().create_run_from_pipeline_func(pipeline_func, {'dataset_path': '/data/mnist'})

import mlflow
from evidently import ColumnMapping
from evidently.model_profile import Profile
from evidently.model_profile.sections import DataDriftProfileSection

# Load data
import pandas as pd
reference_data = pd.read_csv('reference.csv')
current_data = pd.read_csv('current.csv')

# Data drift detection
profile = Profile(sections=[DataDriftProfileSection()])
profile.calculate(reference_data, current_data)
drift_report = profile.json()

# Log drift results
mlflow.log_text(drift_report, "data_drift.json")
print("Drift detection complete and logged.")

from sagemaker.model_monitor import DefaultModelMonitor

monitor = DefaultModelMonitor(
    role=role,
    instance_count=1,
    instance_type="ml.m5.large"
)

monitor.create_monitoring_schedule(
    endpoint_input="my-endpoint",
    schedule_cron_expression="cron(0 * ? * * *)",
    output_s3_uri="s3://my-bucket/monitoring"
)
print("Security monitoring scheduled.")

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: pipeline-executor
rules:
- apiGroups: [""]
  resources: ["pods", "secrets"]
  verbs: ["create", "get", "list"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: pipeline-binding
subjects:
- kind: User
  name: pipeline-user
  apiGroup: rbac.authorization.k8s.io
roleRef:
  kind: Role
  name: pipeline-executor
  apiGroup: rbac.authorization.k8s.io

mlflow models serve \
  -m models:/BostonHousing/1 \
  --host 0.0.0.0 --port 1234 \
  --certfile /path/to/cert.pem \
  --keyfile /path/to/key.pem

from sagemaker.sklearn.model import SKLearnModel

model = SKLearnModel(
    model_data='s3://my-bucket/model.tar.gz',
    role=role
)
predictor = model.deploy(instance_type="ml.m5.large")

shap_values = predictor.explain(input_data)
print("SHAP explainability results logged.")

import hashlib

def validate_model(file_path):
    with open(file_path, 'rb') as f:
        file_hash = hashlib.sha256(f.read()).hexdigest()
    assert file_hash == "expected_hash", "Model integrity check failed!"
    print("Model integrity verified.")

# .github/workflows/aisecops_prompts.yml
name: AISecOps Prompt-Driven Security Pipeline

on: [push, pull_request]

jobs:
  security_analysis:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Run AI Security Prompts
        env:
          OPENROUTER_API_KEY: ${{ secrets.OPENROUTER_API_KEY }}
        run: |
          python aisecops/prompt_runner.py \
            --stage plan \
            --prompt-type threat_modeling \
            --output security_report.json

import os
import json
import argparse
from openrouter import OpenRouter
from fabric import Fabric

class AISecOpsPromptRunner:
    def __init__(self, api_key=None):
        self.openrouter = OpenRouter(api_key or os.getenv('OPENROUTER_API_KEY'))
        self.fabric = Fabric()
    
    def load_prompt_template(self, stage, prompt_type):
        """
        Load predefined prompt templates based on stage and type
        """
        prompt_templates = {
            'plan': {
                'threat_modeling': {
                    'model': 'anthropic/claude-2',
                    'template': """
                    Comprehensive Threat Modeling Analysis:
                    1. Identify potential attack vectors for {project_type}
                    2. Provide risk score (1-10) for each identified threat
                    3. Recommend mitigation strategies
                    """
                }
            },
            'code': {
                'secure_authentication': {
                    'model': 'openai/gpt-4',
                    'template': """
                    Generate a secure authentication module with:
                    1. Multi-factor authentication implementation
                    2. Secure password hashing
                    3. Rate limiting mechanism
                    """
                }
            }
            # Add more stages and prompt types
        }
        
        return prompt_templates.get(stage, {}).get(prompt_type, {})
    
    def run_prompt(self, stage, prompt_type, context=None):
        """
        Execute AI-powered prompt with context
        """
        prompt_config = self.load_prompt_template(stage, prompt_type)
        
        if not prompt_config:
            raise ValueError(f"No prompt template found for {stage}/{prompt_type}")
        
        # Prepare context
        context = context or {}
        prompt = prompt_config['template'].format(**context)
        
        # Generate response using OpenRouter
        response = self.openrouter.generate(
            model=prompt_config['model'],
            prompt=prompt
        )
        
        # Enhance with Fabric AI
        enhanced_response = self.fabric.analyze(
            content=response,
            task=f"Security Analysis for {stage}/{prompt_type}"
        )
        
        return {
            'original_response': response,
            'enhanced_response': enhanced_response,
            'metadata': {
                'stage': stage,
                'prompt_type': prompt_type,
                'model': prompt_config['model']
            }
        }
    
    def save_report(self, results, output_file='aisecops_report.json'):
        """
        Save analysis results to a JSON file
        """
        with open(output_file, 'w') as f:
            json.dump(results, f, indent=2)
        
        print(f"Report saved to {output_file}")

def main():
    parser = argparse.ArgumentParser(description='AISecOps Prompt Runner')
    parser.add_argument('--stage', required=True, help='DevOps stage')
    parser.add_argument('--prompt-type', required=True, help='Prompt type')
    parser.add_argument('--output', default='aisecops_report.json', help='Output report file')
    
    args = parser.parse_args()
    
    runner = AISecOpsPromptRunner()
    results = runner.run_prompt(
        stage=args.stage, 
        prompt_type=args.prompt_type
    )
    
    runner.save_report(results, args.output)

if __name__ == '__main__':
    main()